OpenRaiser/Intern-Atlas huggingface dataset:
The ArborX library: version 2.0
Venue: arXiv cs.DC 2025
Year: 2025

This paper provides an overview of the 2.0 release of the ArborX library, a performance portable geometric search library based on Kokkos. We describe the major changes in ArborX 2.0 including a new interface for the library to support a wider range of user problems, new search data structures (brute force, distributed), support for user functions to be executed on the results (callbacks), and an expanded set of the supported algorithms (ray tracing, clustering).
TextQuests: How Good are LLMs at Text-Based Video Games?
Venue: arXiv cs.AI 2025
Year: 2025

Evaluating AI agents within complex, interactive environments that mirror real-world challenges is critical for understanding their practical capabilities. While existing agent benchmarks effectively assess skills like tool use or performance on structured tasks, they often do not fully capture an agent's ability to operate autonomously in exploratory environments that demand sustained, self-directed reasoning over a long and growing context. To enable a more accurate assessment of AI agents in challenging exploratory environments, we introduce TextQuests, a benchmark based on the Infocom suite of interactive fiction games. These text-based adventures, which can take human players over 30 hours and require hundreds of precise actions to solve, serve as an effective proxy for evaluating AI agents on focused, stateful tasks. The benchmark is specifically designed to assess an LLM agent's capacity for self-contained problem-solving by precluding the use of external tools, thereby focusing on intrinsic long-context reasoning capabilities in an exploratory environment characterized by the need for trial-and-error learning and sustained problem-solving within a single interactive session. We release TextQuests at https://textquests.ai.
Explainable Image Classification with Reduced Overconfidence for Tissue Characterisation
Venue: arXiv cs.CV 2025
Year: 2025

The deployment of Machine Learning models intraoperatively for tissue characterisation can assist decision making and guide safe tumour resections. For image classification models, pixel attribution methods are popular to infer explainability. However, overconfidence in deep learning model's predictions translates to overconfidence in pixel attribution. In this paper, we propose the first approach which incorporates risk estimation into a pixel attribution method for improved image classification explainability. The proposed method iteratively applies a classification model with a pixel attribution method to create a volume of PA maps. This volume is used for the first time, to generate a pixel-wise distribution of PA values. We introduce a method to generate an enhanced PA map by estimating the expectation values of the pixel-wise distributions. In addition, the coefficient of variation (CV) is used to estimate pixel-wise risk of this enhanced PA map. Hence, the proposed method not only provides an improved PA map but also produces an estimation of risk on the output PA values. Performance evaluation on probe-based Confocal Laser Endomicroscopy (pCLE) data and ImageNet verifies that our improved explainability method outperforms the state-of-the-art.
Informing AI Risk Assessment with News Media: Analyzing National and Political Variation in the Coverage of AI Risks
Venue: arXiv cs.CY 2025
Year: 2025

Risk-based approaches to AI governance often center the technological artifact as the primary focus of risk assessments, overlooking systemic risks that emerge from the complex interaction between AI systems and society. One potential source to incorporate more societal context into these approaches is the news media, as it embeds and reflects complex interactions between AI systems, human stakeholders, and the larger society. News media is influential in terms of which AI risks are emphasized and discussed in the public sphere, and thus which risks are deemed important. Yet, variations in the news media between countries and across different value systems (e.g. political orientations) may differentially shape the prioritization of risks through the media's agenda setting and framing processes. To better understand these variations, this work presents a comparative analysis of a cross-national sample of news media spanning 6 countries (the U.S., the U.K., India, Australia, Israel, and South Africa). Our findings show that AI risks are prioritized differently across nations and shed light on how left vs. right leaning U.S. based outlets not only differ in the prioritization of AI risks in their coverage, but also use politicized language in the reporting of these risks. These findings can inform risk assessors and policy-makers about the nuances they should account for when considering news media as a supplementary source for risk-based governance approaches.
Rule2Text: Natural Language Explanation of Logical Rules in Knowledge Graphs
Venue: arXiv cs.CL 2025
Year: 2025

Knowledge graphs (KGs) often contain sufficient information to support the inference of new facts. Identifying logical rules not only improves the completeness of a knowledge graph but also enables the detection of potential errors, reveals subtle data patterns, and enhances the overall capacity for reasoning and interpretation. However, the complexity of such rules, combined with the unique labeling conventions of each KG, can make them difficult for humans to understand. In this paper, we explore the potential of large language models to generate natural language explanations for logical rules. Specifically, we extract logical rules using the AMIE 3.5.1 rule discovery algorithm from the benchmark dataset FB15k-237 and two large-scale datasets, FB-CVT-REV and FB+CVT-REV. We examine various prompting strategies, including zero- and few-shot prompting, including variable entity types, and chain-of-thought reasoning. We conduct a comprehensive human evaluation of the generated explanations based on correctness, clarity, and hallucination, and also assess the use of large language models as automatic judges. Our results demonstrate promising performance in terms of explanation correctness and clarity, although several challenges remain for future research. All scripts and data used in this study are publicly available at https://github.com/idirlab/KGRule2NL}{https://github.com/idirlab/KGRule2NL.
CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks
Venue: arXiv cs.AI 2025
Year: 2025

We propose CoT-Self-Instruct, a synthetic data generation method that instructs LLMs to first reason and plan via Chain-of-Thought (CoT) based on given seed tasks, and then generate a new synthetic example of similar quality and complexity. This is followed by a filtering step to select high-quality data using automatic metrics, which are then used for LLM training. In verifiable reasoning, our synthetic data significantly outperforms existing training datasets, such as s1k and OpenMathReasoning, when evaluated on MATH500, AMC23, AIME24, and GPQA-Diamond. For non-verifiable instruction-following tasks, our method surpasses the performance of both human and standard Self-Instruct training data on the AlpacaEval 2.0 and Arena-Hard benchmarks.
SeqAffordSplat: Scene-level Sequential Affordance Reasoning on 3D Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

3D affordance reasoning, the task of associating human instructions with the functional regions of 3D objects, is a critical capability for embodied agents. Current methods based on 3D Gaussian Splatting (3DGS) are fundamentally limited to single-object, single-step interactions, a paradigm that falls short of addressing the long-horizon, multi-object tasks required for complex real-world applications. To bridge this gap, we introduce the novel task of Sequential 3D Gaussian Affordance Reasoning and establish SeqAffordSplat, a large-scale benchmark featuring 1800+ scenes to support research on long-horizon affordance understanding in complex 3DGS environments. We then propose SeqSplatNet, an end-to-end framework that directly maps an instruction to a sequence of 3D affordance masks. SeqSplatNet employs a large language model that autoregressively generates text interleaved with special segmentation tokens, guiding a conditional decoder to produce the corresponding 3D mask. To handle complex scene geometry, we introduce a pre-training strategy, Conditional Geometric Reconstruction, where the model learns to reconstruct complete affordance region masks from known geometric observations, thereby building a robust geometric prior. Furthermore, to resolve semantic ambiguities, we design a feature injection mechanism that lifts rich semantic features from 2D Vision Foundation Models (VFM) and fuses them into the 3D decoder at multiple scales. Extensive experiments demonstrate that our method sets a new state-of-the-art on our challenging benchmark, effectively advancing affordance reasoning from single-step interactions to complex, sequential tasks at the scene level.
Extended Abstract: Mutable Objects with Several Implementations
Venue: arXiv cs.PL 2025
Year: 2025

This extended abstract outlines an ACL2 feature, attach-stobj, that first appeared in ACL2 Version 8.6 (October, 2024). This feature supports different executable operations for a given abstract stobj, without requiring recertification of the book that introduces that stobj or theorems about it. The paper provides background as well as a user-level overview and some implementation notes.
Strategic Communication and Language Bias in Multi-Agent LLM Coordination
Venue: arXiv cs.MA 2025
Year: 2025

Large Language Model (LLM)-based agents are increasingly deployed in multi-agent scenarios where coordination is crucial but not always assured. Research shows that the way strategic scenarios are framed linguistically can affect cooperation. This paper explores whether allowing agents to communicate amplifies these language-driven effects. Leveraging FAIRGAME, we simulate one-shot and repeated games across different languages and models, both with and without communication. Our experiments, conducted with two advanced LLMs-GPT-4o and Llama 4 Maverick-reveal that communication significantly influences agent behavior, though its impact varies by language, personality, and game structure. These findings underscore the dual role of communication in fostering coordination and reinforcing biases.
Hybrid LSTM-Transformer Models for Profiling Highway-Railway Grade Crossings
Venue: arXiv cs.LG 2025
Year: 2025

Hump crossings, or high-profile Highway Railway Grade Crossings (HRGCs), pose safety risks to highway vehicles due to potential hang-ups. These crossings typically result from post-construction railway track maintenance activities or non-compliance with design guidelines for HRGC vertical alignments. Conventional methods for measuring HRGC profiles are costly, time-consuming, traffic-disruptive, and present safety challenges. To address these issues, this research employed advanced, cost-effective techniques and innovative modeling approaches for HRGC profile measurement. A novel hybrid deep learning framework combining Long Short-Term Memory (LSTM) and Transformer architectures was developed by utilizing instrumentation and ground truth data. Instrumentation data were gathered using a highway testing vehicle equipped with Inertial Measurement Unit (IMU) and Global Positioning System (GPS) sensors, while ground truth data were obtained via an industrial-standard walking profiler. Field data was collected at the Red Rock Railroad Corridor in Oklahoma. Three advanced deep learning models Transformer-LSTM sequential (model 1), LSTM-Transformer sequential (model 2), and LSTM-Transformer parallel (model 3) were evaluated to identify the most efficient architecture. Models 2 and 3 outperformed the others and were deployed to generate 2D/3D HRGC profiles. The deep learning models demonstrated significant potential to enhance highway and railroad safety by enabling rapid and accurate assessment of HRGC hang-up susceptibility.
Towards Reliable AI in 6G: Detecting Concept Drift in Wireless Network
Venue: arXiv cs.NI 2025
Year: 2025

AI-native 6G networks promise unprecedented automation and performance by embedding machine-learning models throughout the radio access and core segments of the network. However, the non-stationary nature of wireless environments due to infrastructure changes, user mobility, and emerging traffic patterns, induces concept drifts that can quickly degrade these model accuracies. Existing methods in general are very domain specific, or struggle with certain type of concept drift. In this paper, we introduce two unsupervised, model-agnostic, batch concept drift detectors. Both methods compute an expected-utility score to decide when concept drift occurred and if model retraining is warranted, without requiring ground-truth labels after deployment. We validate our framework on two real-world wireless use cases in outdoor fingerprinting for localization and for link-anomaly detection, and demonstrate that both methods are outperforming classical detectors such as ADWIN, DDM, CUSUM by 20-40 percentage points. Additionally, they achieve an F1-score of 0.94 and 1.00 in correctly triggering retraining alarm, thus reducing the false alarm rate by up to 20 percentage points compared to the best classical detectors.
Machine Learning Pipeline for Software Engineering: A Systematic Literature Review
Venue: arXiv cs.SE 2025
Year: 2025

The rapid advancement of software development practices has introduced challenges in ensuring quality and efficiency across the software engineering (SE) lifecycle. As SE systems grow in complexity, traditional approaches often fail to scale, resulting in longer debugging times, inefficient defect detection, and resource-heavy development cycles. Machine Learning (ML) has emerged as a key solution, enabling automation in tasks such as defect prediction, code review, and release quality estimation. However, the effectiveness of ML in SE depends on the robustness of its pipeline, including data collection, preprocessing, feature engineering, algorithm selection, validation, and evaluation. This systematic literature review (SLR) examines state-of-the-art ML pipelines designed for SE, consolidating best practices, challenges, and gaps. Our findings show that robust preprocessing, such as SMOTE for data balancing and SZZ-based algorithms for feature selection, improves model reliability. Ensemble methods like Random Forest and Gradient Boosting dominate performance across tasks, while simpler models such as Naive Bayes remain valuable for efficiency and interpretability. Evaluation metrics including AUC, F1-score, and precision are most common, with new metrics like Best Arithmetic Mean (BAM) emerging in niche applications. Validation techniques such as bootstrapping are widely used to ensure model stability and generalizability. This SLR highlights the importance of well-designed ML pipelines for addressing SE challenges and provides actionable insights for researchers and practitioners seeking to optimize software quality and efficiency. By identifying gaps and trends, this study sets a foundation for advancing ML adoption and fostering innovation in increasingly complex development environments.
TriP-LLM: A Tri-Branch Patch-wise Large Language Model Framework for Time-Series Anomaly Detection
Venue: arXiv cs.LG 2025
Year: 2025

Time-series anomaly detection plays a central role across a wide range of application domains. With the increasing proliferation of the Internet of Things (IoT) and smart manufacturing, time-series data has dramatically increased in both scale and dimensionality. This growth has exposed the limitations of traditional statistical methods in handling the high heterogeneity and complexity of such data. Inspired by the recent success of large language models (LLMs) in multimodal tasks across language and vision domains, we propose a novel unsupervised anomaly detection framework: A Tri-Branch Patch-wise Large Language Model Framework for Time-Series Anomaly Detection (TriP-LLM). TriP-LLM integrates local and global temporal features through a triple-branch design comprising Patching, Selecting, and Global modules, to encode the input time-series into patch-wise representations, which are then processed by a frozen, pretrained LLM. A lightweight patch-wise decoder reconstructs the input, from which anomaly scores are derived. We evaluate TriP-LLM on several public benchmark datasets using PATE, a recently proposed threshold-free evaluation metric, and conduct all comparisons within a unified open-source framework to ensure fairness. Experimental results show that TriP-LLM consistently outperforms recent state-of-the-art (SOTA) methods across all datasets, demonstrating strong detection capabilities. Furthermore, through extensive ablation studies, we verify the substantial contribution of the LLM to the overall architecture. Compared to LLM-based approaches using Channel Independence (CI) patch processing, TriP-LLM achieves significantly lower memory consumption, making it more suitable for GPU memory-constrained environments. All code and model checkpoints of TriP-LLM are publicly available on https://github.com/YYZStart/TriP-LLM.git
PhysicsEval: Inference-Time Techniques to Improve the Reasoning Proficiency of Large Language Models on Physics Problems
Venue: arXiv cs.CL 2025
Year: 2025

The discipline of physics stands as a cornerstone of human intellect, driving the evolution of technology and deepening our understanding of the fundamental principles of the cosmos. Contemporary literature includes some works centered on the task of solving physics problems - a crucial domain of natural language reasoning. In this paper, we evaluate the performance of frontier LLMs in solving physics problems, both mathematical and descriptive. We also employ a plethora of inference-time techniques and agentic frameworks to improve the performance of the models. This includes the verification of proposed solutions in a cumulative fashion by other, smaller LLM agents, and we perform a comparative analysis of the performance that the techniques entail. There are significant improvements when the multi-agent framework is applied to problems that the models initially perform poorly on. Furthermore, we introduce a new evaluation benchmark for physics problems, ${\rm P{\small HYSICS}E{\small VAL}}$, consisting of 19,609 problems sourced from various physics textbooks and their corresponding correct solutions scraped from physics forums and educational websites. Our code and data are publicly available at https://github.com/areebuzair/PhysicsEval.
Rethinking Evidence Hierarchies in Medical Language Benchmarks: A Critical Evaluation of HealthBench
Venue: arXiv cs.AI 2025
Year: 2025

HealthBench, a benchmark designed to measure the capabilities of AI systems for health better (Arora et al., 2025), has advanced medical language model evaluation through physician-crafted dialogues and transparent rubrics. However, its reliance on expert opinion, rather than high-tier clinical evidence, risks codifying regional biases and individual clinician idiosyncrasies, further compounded by potential biases in automated grading systems. These limitations are particularly magnified in low- and middle-income settings, where issues like sparse neglected tropical disease coverage and region-specific guideline mismatches are prevalent. The unique challenges of the African context, including data scarcity, inadequate infrastructure, and nascent regulatory frameworks, underscore the urgent need for more globally relevant and equitable benchmarks. To address these shortcomings, we propose anchoring reward functions in version-controlled Clinical Practice Guidelines (CPGs) that incorporate systematic reviews and GRADE evidence ratings. Our roadmap outlines "evidence-robust" reinforcement learning via rubric-to-guideline linkage, evidence-weighted scoring, and contextual override logic, complemented by a focus on ethical considerations and the integration of delayed outcome feedback. By re-grounding rewards in rigorously vetted CPGs, while preserving HealthBench's transparency and physician engagement, we aim to foster medical language models that are not only linguistically polished but also clinically trustworthy, ethically sound, and globally relevant.
XRoboToolkit: A Cross-Platform Framework for Robot Teleoperation
Venue: arXiv cs.RO 2025
Year: 2025

The rapid advancement of Vision-Language-Action models has created an urgent need for large-scale, high-quality robot demonstration datasets. Although teleoperation is the predominant method for data collection, current approaches suffer from limited scalability, complex setup procedures, and suboptimal data quality. This paper presents XRoboToolkit, a cross-platform framework for extended reality based robot teleoperation built on the OpenXR standard. The system features low-latency stereoscopic visual feedback, optimization-based inverse kinematics, and support for diverse tracking modalities including head, controller, hand, and auxiliary motion trackers. XRoboToolkit's modular architecture enables seamless integration across robotic platforms and simulation environments, spanning precision manipulators, mobile robots, and dexterous hands. We demonstrate the framework's effectiveness through precision manipulation tasks and validate data quality by training VLA models that exhibit robust autonomous performance.
Structured Transformations for Stable and Interpretable Neural Computation
Venue: arXiv cs.LG 2025
Year: 2025

Despite their impressive performance, contemporary neural networks often lack structural safeguards that promote stable learning and interpretable behavior. In this work, we introduce a reformulation of layer-level transformations that departs from the standard unconstrained affine paradigm. Each transformation is decomposed into a structured linear operator and a residual corrective component, enabling more disciplined signal propagation and improved training dynamics. Our formulation encourages internal consistency and supports stable information flow across depth, while remaining fully compatible with standard learning objectives and backpropagation. Through a series of synthetic and real-world experiments, we demonstrate that models constructed with these structured transformations exhibit improved gradient conditioning, reduced sensitivity to perturbations, and layer-wise robustness. We further show that these benefits persist across architectural scales and training regimes. This study serves as a foundation for a more principled class of neural architectures that prioritize stability and transparency-offering new tools for reasoning about learning behavior without sacrificing expressive power.